convert paddle op definations into pd dialect in infrt (#38708)

paddle/infrt/dialect/pd_op_base.td

@@ -73,5 +73,6 @@ def PD_ElementType : Type<Or<[PD_Float.predicate,
 
 def PD_Tensor : TensorOf<[PD_ElementType]>;
 
+def PD_Tensor_Array : VectorOf<[PD_Tensor]>;
 
 #endif // PD_OP_BASE

paddle/infrt/dialect/pd_ops.cc

@@ -25,6 +25,7 @@
 
 namespace mlir {
 namespace pd {
+
 PaddleDialect::PaddleDialect(MLIRContext *context)
     : Dialect("pd", context, TypeID::get<PaddleDialect>()) {
   addOperations<
@@ -69,7 +70,7 @@ mlir::OpFoldResult ConstantOp::fold(
     ::llvm::ArrayRef<mlir::Attribute> operands) {
   return value();
 }
-
+/*
 LogicalResult ElementwiseAdd::inferReturnTypes(
     MLIRContext *context,
     Optional<Location> location,
@@ -165,7 +166,7 @@ void FusedRepeatedFCRelu::getCanonicalizationPatterns(
 void BatchNormOp::getCanonicalizationPatterns(
     mlir::OwningRewritePatternList &results, mlir::MLIRContext *context) {
   results.insert<FuseBatchNormWithConvPattern>(context);
-}
+}*/
 
 }  // namespace pd
 }  // namespace mlir

paddle/infrt/dialect/pd_ops.td

-#ifndef PD_OPS
-#define PD_OPS
-
-include "mlir/Interfaces/InferTypeOpInterface.td"
-include "mlir/Interfaces/LoopLikeInterface.td"
-include "mlir/IR/OpBase.td"
-include "paddle/infrt/dialect/pd_op_base.td"
-
-def PD_FeedOp : PD_Op<"feed"> {
-  let summary = "Feed Op";
-
-  let description = [{
-    Feed a tensor into the model.
-  }];
-
-  let arguments = (ins StrAttr:$name);
-  let results = (outs PD_Tensor:$out);
-
-  let assemblyFormat = [{
-      `(` `)` attr-dict `:` type($out)
-  }];
-}
-
-def PD_FetchOp : PD_Op<"fetch", [Terminator]> {
-  let summary = "fetch Op";
-
-  let description = [{
-    Return the output tensor from the subgraph.
-  }];
-
-  let arguments = (ins PD_Tensor :$inputs, StrAttr:$name);
-}
-
-def PD_ReturnOp : PD_Op<"return", [Terminator]> {
-  let summary = "return Op";
-
-  let description = [{
-    Fetch tensor from the graph.
-  }];
-
-  let arguments = (ins Variadic<PD_Tensor>:$inputs);
-}
-
-def PD_GraphOp : PD_Op<"graph", [SingleBlockImplicitTerminator<"ReturnOp">]> {
-  let summary = "paddle graph Op";
-  let description = [{
-    Describe a paddle graph or subgraph.
-  }];
-  let regions = (region SizedRegion<1>:$body);
-  let arguments = (ins Variadic<PD_Tensor>:$inputs);
-  let results = (outs Variadic<PD_Tensor>:$outputs);
-}
-
-def PD_ConstantOp : PD_Op<"constant", [NoSideEffect, ConstantLike, DeclareOpInterfaceMethods<InferTypeOpInterface>, AllTypesMatch<["value", "output"]>]> {
-  let summary = "constant Op";
-  let description = [{}];
-
-  let arguments = (ins ElementsAttr:$value);
-  let results = (outs PD_Tensor:$output);
-  let hasFolder = 1;
-
-  let builders = [
-    OpBuilder<(ins "Attribute":$value)>,
-  ];
-}
-
-def PD_AbsOp : PD_Op<"abs", [NoSideEffect, SameOperandsAndResultType]> {
-  let summary = "Computes the absolute value of a tensor";
-
-  let description = [{
-  }];
-
-  let arguments = (ins PD_Tensor:$x);
-  let results = (outs PD_Tensor:$y);
-}
-
-def PD_SqrtOp : PD_Op<"sqrt", [NoSideEffect, SameOperandsAndResultType]> {
-  let summary = "Computes the sqrt value of a tensor";
-
-  let description = [{
-  }];
-
-  let arguments = (ins PD_Tensor:$x);
-  let results = (outs PD_Tensor:$y);
-}
-
-def PD_ReluOp : PD_Op<"relu", [NoSideEffect, SameOperandsAndResultType]> {
-  let summary = "Computes the Relu of a tensor";
-
-  let description = [{
-  }];
-
-  let arguments = (ins PD_Tensor:$x);
-  let results = (outs PD_Tensor:$y);
-  let hasCanonicalizer = 1;
-}
-
-def PD_Relu6Op : PD_Op<"relu6", [NoSideEffect, SameOperandsAndResultType]> {
-  let summary = "Computes the Relu6 of a tensor";
-
-  let description = [{
-  }];
-
-  let arguments = (ins PD_Tensor:$x);
-  let results = (outs PD_Tensor:$y);
-}
-
-def PD_ElementwiseAdd : PD_Op<"elementwise_add", [NoSideEffect, Commutative, DeclareOpInterfaceMethods<InferTypeOpInterface>]> {
-  let summary = "ElementwiseAdd Op";
-  let description = [{
-  }];
-
-  let arguments = (ins PD_Tensor:$x, PD_Tensor:$y, DefaultValuedAttr<I32Attr, "-1">:$axis);
-  let results = (outs PD_Tensor:$out);
-  let hasCanonicalizer = 1;
-  let hasFolder = 1;
-}
-
-def PD_ElementwiseSub : PD_Op<"elementwise_sub", [NoSideEffect, DeclareOpInterfaceMethods<InferTypeOpInterface>]> {
-  let summary = "ElementwiseSub Op";
-  let description = [{
-  }];
-
-  let arguments = (ins PD_Tensor:$x, PD_Tensor:$y, DefaultValuedAttr<I32Attr, "-1">:$axis);
-  let results = (outs PD_Tensor:$out);
-}
-
-def PD_ElementwiseMul : PD_Op<"elementwise_mul", [NoSideEffect, Commutative, DeclareOpInterfaceMethods<InferTypeOpInterface>]> {
-  let summary = "ElementwiseMul Op";
-  let description = [{
-  }];
-
-  let arguments = (ins PD_Tensor:$x, PD_Tensor:$y, DefaultValuedAttr<I32Attr, "-1">:$axis);
-  let results = (outs PD_Tensor:$out);
-}
-
-def PD_ElementwiseDiv : PD_Op<"elementwise_div", [NoSideEffect, DeclareOpInterfaceMethods<InferTypeOpInterface>]> {
-  let summary = "ElementwiseDiv Op";
-  let description = [{
-  }];
-
-  let arguments = (ins PD_Tensor:$x, PD_Tensor:$y, DefaultValuedAttr<I32Attr, "-1">:$axis);
-  let results = (outs PD_Tensor:$out);
-}
-
-def PD_MatmulOp : PD_Op<"matmul", [NoSideEffect]> {
-  let summary = "Computes the matrix mulplication result of two tensors";
-  let description = [{
-  }];
-
-  let arguments = (ins PD_Tensor:$x, PD_Tensor:$y,
-                  DefaultValuedAttr<BoolAttr, "false">:$transpose_x,
-                  DefaultValuedAttr<BoolAttr, "false">:$transpose_y,
-                  DefaultValuedAttr<F32Attr, "1.0">:$alpha);
-  let results = (outs PD_Tensor:$out);
-
-  //let hasCanonicalizer = 1;
-}
-
-def PD_MulOp : PD_Op<"mul", [NoSideEffect, DeclareOpInterfaceMethods<InferTypeOpInterface>]> {
-  let summary = "paddle mul op";
-  let description = [{}];
-
-  let arguments = (ins PD_Tensor:$x, PD_Tensor:$y);
-  let results = (outs PD_Tensor:$out);
-
-  //let hasCanonicalizer = 1;
-}
-
-def PD_Conv2dOp : PD_Op<"conv2d", [NoSideEffect]> {
-  let summary = "paddle conv2d operation";
-  let description = [{
-  }];
-
-  let arguments = (ins PD_Tensor:$Input, PD_Tensor:$Filter, PD_Tensor:$Bias);
-  let results = (outs PD_Tensor:$Output);
-
-  //let hasCanonicalizer = 1;
-}
-
-def PD_BatchNormOp : PD_Op<"batch_norm", [NoSideEffect]> {
-  let summary = "paddle batch_norm operation";
-  let description = [{
-  }];
-
-  let arguments = (ins PD_Tensor:$X, PD_Tensor:$Scale, PD_Tensor:$Bias,
-                   PD_Tensor:$Mean, PD_Tensor:$Variance,
-                   DefaultValuedAttr<F32Attr, "1e-05">:$epsilon);
-  let results = (outs PD_Tensor:$Y);
-
-  let hasCanonicalizer = 1;
-}
-
-def PD_FusedFC : PD_Op<"fc", [NoSideEffect]> {
-    let summary = "Computes the Fully Connected result of two tensors";
-    let description = [{
-    }];
-
-    let arguments = (ins PD_Tensor:$input, PD_Tensor:$w, PD_Tensor:$bias, DefaultValuedAttr<I32Attr, "1">:$in_num_col_dims);
-    let results = (outs PD_Tensor:$out);
-}
-
-def PD_FusedRepeatedFCRelu : PD_Op<"fusion_repeated_fc_relu", [SameVariadicOperandSize, NoSideEffect]> {
-    let summary = "";
-    let description = [{ }];
-
-    let arguments = (ins PD_Tensor:$input, Variadic<PD_Tensor>:$w, Variadic<PD_Tensor>:$bias);
-    let results = (outs PD_Tensor:$out);
-    let hasCanonicalizer = 1;
-}
-
-#endif  // PD_OPS

paddle/infrt/dialect/rewrite.td

@@ -4,7 +4,7 @@
 include "paddle/infrt/dialect/infrt_base.td"
 include "mlir/Interfaces/SideEffectInterfaces.td"
 include "paddle/infrt/dialect/pd_ops.td"
-
+/*
 //===----------------------------------------------------------------------===//
 // This is to fuse the composition: 'Matmul o ElementwiseAdd' into 'PD_FusedFC'.
 //
@@ -89,5 +89,5 @@ def FuseBatchNormWithConvPattern: Pat<
                 $coefficientW),
             (INFRT_createI32Attr<"1">)))
 >;
-
+*/
 #endif // INFRT_REWRITE

paddle/scripts/infrt_build.sh

@@ -18,7 +18,7 @@
 #                   Utils
 #=================================================
 
-set -ex
+set -e
 
 if [ -z ${BRANCH} ]; then
     BRANCH="develop"
@@ -27,6 +27,22 @@ fi
 EXIT_CODE=0;
 tmp_dir=`mktemp -d`
 
+function update_pd_ops() {
+   PADDLE_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}")/../../" && pwd )"
+   # compile and install paddle
+   rm -rf ${PADDLE_ROOT}/build && mkdir -p ${PADDLE_ROOT}/build
+   cd ${PADDLE_ROOT}/build
+   cmake .. -DWITH_PYTHON=ON -DWITH_GPU=OFF -DPYTHON_EXECUTABLE=`which python3`
+   make -j8
+   cd ${PADDLE_ROOT}/build
+   cd python/dist/
+   python3 -m pip uninstall -y paddlepaddle
+   python3 -m pip install  *whl
+   # update pd_ops.td
+   cd ${PADDLE_ROOT}/tools/infrt/
+   python3 generate_pd_op_dialect_from_paddle_op_maker.py
+}
+
 function init() {
     RED='\033[0;31m'
     BLUE='\033[0;34m'
@@ -62,7 +78,11 @@ function infrt_gen_and_build() {
     fi
     startTime_s=`date +%s`
     set +e
+
     mkdir -p ${PADDLE_ROOT}/build
+    # step1. reinstall paddle and generate pd_ops.td
+    update_pd_ops
+    # step2. compile infrt
     cd ${PADDLE_ROOT}/build
     rm -f infrt_summary.txt
     cmake ..  -DWITH_MKL=OFF -DWITH_GPU=OFF -DWITH_CRYPTO=OFF -DCMAKE_BUILD_TYPE=Release -DWITH_INFRT=ON -DWITH_PYTHON=OFF -DWITH_TESTING==${WITH_TESTING:-ON}; build_error=$?
@@ -104,9 +124,19 @@ EOF
 }
 
 function main() {
-    local CMD=$1 
+    local CMD=$1
     local parallel_number=$2
+    if [ -z "$1" ]; then
+        echo "Usage:"
+        echo "      (1)bash infrt_build.sh build_and_test"
+        echo "      (2)bash infrt_build.sh build_only"
+        echo "      (3)bash infrt_build.sh test_only"
+        echo "      optional command: --update_pd_ops : pd_ops.td will be updated according to paddle's code."
+        exit 0
+    fi
+
     init
+
     case $CMD in
       build_and_test)
         infrt_gen_and_build ${parallel_number}
@@ -122,14 +152,15 @@ function main() {
         print_usage
         exit 1
         ;;
-      esac
-      set +x
-      if [[ -f ${PADDLE_ROOT}/build/infrt_summary.txt ]];then
-        echo "=====================build summary======================"
-        cat ${PADDLE_ROOT}/build/infrt_summary.txt
-        echo "========================================================"
-      fi
-      echo "paddle_build script finished as expected!"
+    esac
+
+    set +x
+    if [[ -f ${PADDLE_ROOT}/build/infrt_summary.txt ]];then
+      echo "=====================build summary======================"
+      cat ${PADDLE_ROOT}/build/infrt_summary.txt
+      echo "========================================================"
+    fi
+    echo "paddle_build script finished as expected!"
 }
 
 main $@

tools/infrt/custom_pdop.td

+def PD_FeedOp : PD_Op<"feed"> {
+  let summary = "Feed Op";
+
+  let description = [{
+    Feed a tensor into the model.
+  }];
+
+  let arguments = (ins StrAttr:$name);
+  let results = (outs PD_Tensor:$out);
+
+  let assemblyFormat = [{
+      `(` `)` attr-dict `:` type($out)
+  }];
+}
+
+def PD_FetchOp : PD_Op<"fetch", [Terminator]> {
+  let summary = "fetch Op";
+
+  let description = [{
+    Return the output tensor from the subgraph.
+  }];
+
+  let arguments = (ins PD_Tensor :$inputs, StrAttr:$name);
+}
+
+def PD_ReturnOp : PD_Op<"return", [Terminator]> {
+  let summary = "return Op";
+
+  let description = [{
+    Fetch tensor from the graph.
+  }];
+
+  let arguments = (ins Variadic<PD_Tensor>:$inputs);
+}
+
+def PD_GraphOp : PD_Op<"graph", [SingleBlockImplicitTerminator<"ReturnOp">]> {
+  let summary = "paddle graph Op";
+  let description = [{
+    Describe a paddle graph or subgraph.
+  }];
+  let regions = (region SizedRegion<1>:$body);
+  let arguments = (ins Variadic<PD_Tensor>:$inputs);
+  let results = (outs Variadic<PD_Tensor>:$outputs);
+}
+
+def PD_ConstantOp : PD_Op<"constant", [NoSideEffect, ConstantLike, DeclareOpInterfaceMethods<InferTypeOpInterface>, AllTypesMatch<["value", "output"]>]> {
+  let summary = "constant Op";
+  let description = [{}];
+
+  let arguments = (ins ElementsAttr:$value);
+  let results = (outs PD_Tensor:$output);
+  let hasFolder = 1;
+
+  let builders = [
+    OpBuilder<(ins "Attribute":$value)>,
+  ];
+}

tools/infrt/generate_pd_op_dialect_from_paddle_op_maker.py

+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.fluid.framework as framework
+from paddle.fluid import core
+from paddle import compat as cpt
+
+
+# collect original ops: op which has both inference and grid defination
+def get_original_ops():
+    all_ops, _, _ = core.op_supported_infos('CPU', core.VarDesc.VarType.FP16)
+    grad_ops = []
+    original_ops = []
+
+    for op in all_ops:
+        if op.endswith("_grad"):
+            if op.endswith("_grad_grad"):
+                continue
+            grad_ops.append(op)
+    for op in all_ops:
+        if str(op + "_grad") in grad_ops:
+            original_ops.append(op)
+
+    print("Grad ops num: " + str(len(grad_ops)))
+    print("Responded original ops num: " + str(len(original_ops)))
+    return original_ops
+
+
+# functions of parsing Paddle Proto
+INPUTS = "Inputs"
+OUTPUTS = "Outputs"
+ATTRS = "Attrs"
+COMMENT = "Comment"
+
+DUPLICABLE = "duplicable"
+INTERMEDIATE = "intermediate"
+DISPENSABLE = "dispensable"
+
+TYPE = "type"
+GENERATED = "generated"
+DEFAULT_VALUE = "default_value"
+
+EXTRA = "extra"
+QUANT = "quant"
+
+
+def get_attr_default_value(op_name):
+    return core.get_op_attrs_default_value(cpt.to_bytes(op_name))
+
+
+def get_vars_info(op_vars_proto):
+    vars_info = {}
+    for var_proto in op_vars_proto:
+        name = str(var_proto.name)
+        vars_info[name] = {}
+        vars_info[name][DUPLICABLE] = var_proto.duplicable
+        vars_info[name][DISPENSABLE] = var_proto.dispensable
+        vars_info[name][INTERMEDIATE] = var_proto.intermediate
+        vars_info[name][EXTRA] = var_proto.extra
+        vars_info[name][QUANT] = var_proto.quant
+    return vars_info
+
+
+def get_attrs_info(op_proto, op_attrs_proto):
+    attrs_info = {}
+    attrs_default_values = get_attr_default_value(op_proto.type)
+    for attr_proto in op_attrs_proto:
+        attr_name = str(attr_proto.name)
+        attrs_info[attr_name] = {}
+        attrs_info[attr_name][TYPE] = attr_proto.type
+        attrs_info[attr_name][GENERATED] = attr_proto.generated
+        attrs_info[attr_name][DEFAULT_VALUE] = attrs_default_values[
+            attr_name] if attr_name in attrs_default_values else None
+        attrs_info[attr_name][EXTRA] = attr_proto.extra
+        attrs_info[attr_name][QUANT] = attr_proto.quant
+    return attrs_info
+
+
+def get_op_desc(op_proto):
+    op_info = {}
+    op_info[INPUTS] = get_vars_info(op_proto.inputs)
+    op_info[OUTPUTS] = get_vars_info(op_proto.outputs)
+    op_info[ATTRS] = get_attrs_info(op_proto, op_proto.attrs)
+    op_info[COMMENT] = op_proto.comment
+    return op_info
+
+
+def get_all_ops_desc():
+    all_op_protos_dict = {}
+    all_op_protos = framework.get_all_op_protos()
+    for op_proto in all_op_protos:
+        op_type = str(op_proto.type)
+        all_op_protos_dict[op_type] = get_op_desc(op_proto)
+    return all_op_protos_dict
+
+
+# funtion to generate paddle op dialect file
+def convert_op_proto_into_mlir(op_descs):
+    dst_dialect_file = "../../paddle/infrt/dialect/pd_ops.td"
+    custom_dialect_file = "custom_pdop.td"
+
+    # 1. Head files
+    comment_ = "/*===- TableGen'source file -----------------------------------------------===*\\\n\
+|*                                                                            *|\n\
+|* Op Definitions                                                             *|\n\
+|*                                                                            *|\n\
+|* Automatically generated file, do not edit!                                 *|\n\
+|* Generated by tools/infrt/generate_pd_op_dialect_from_paddle_op_maker.py    *|\n\
+|*                                                                            *|\n\
+\*===----------------------------------------------------------------------===*/\n"
+
+    start_ = comment_ + "#ifndef PD_OPS\n#define PD_OPS\ninclude \"mlir/Interfaces/InferTypeOpInterface.td\"\ninclude \"mlir/Interfaces/LoopLikeInterface.td\"\ninclude \"mlir/IR/OpBase.td\"\ninclude \"paddle/infrt/dialect/pd_op_base.td\"\n\n"
+    with open(dst_dialect_file, 'w') as ops_mlir_file:
+        ops_mlir_file.write(start_)
+
+    # 2. Op dialect
+    # skip list ( ops whose dialect can not be generated automatically will be recorded here)
+    skipped_op_list = [
+        "cos_sim", "fused_embedding_seq_pool", "cosh", "kron", "recurrent",
+        "while", "conditional_block", "set_value", "run_program"
+    ]
+    skipped_attr_list = [
+        "trainable_statistics", "use_global_stats", "is_test", "use_mkldnn",
+        "use_cudnn"
+    ]
+    original_ops_ = get_original_ops()
+    automatically_generated_op_dialect = []
+    for op_type, op_proto in op_descs.items():
+        if (op_type in skipped_op_list) or (op_type not in original_ops_):
+            continue
+        automatically_generated_op_dialect.append(op_type)
+        # 2.1 OpDef
+        HEAD = "def PD_" + op_type.capitalize(
+        ) + "Op : PD_Op<\"" + op_type + "\", [NoSideEffect]> {\n"
+        SUMMARY = "  let summary = \"" + op_type + " op\";\n"
+
+        # 2.2 Description
+        DESCRIPTION = "  let description = [{\n"
+        contents = (op_proto[COMMENT]).split("\n")
+        for line_ in contents:
+            DESCRIPTION = DESCRIPTION + "    " + line_ + "\n"
+        DESCRIPTION += "  }];\n"
+
+        # 2.3 arguments info
+        ARGUMENTS = ""
+        if (len(op_proto[INPUTS]) > 0 or len(op_proto[ATTRS]) > 0):
+            ARGUMENTS = "  let arguments = (ins "
+            # 2.3.1 inputs
+            for input_ in op_proto[INPUTS]:
+                if op_proto[INPUTS][input_][EXTRA] != True and op_proto[INPUTS][
+                        input_][INTERMEDIATE] != True:
+                    if op_proto[INPUTS][input_][DUPLICABLE] != "true":
+                        ARGUMENTS = ARGUMENTS + " PD_Tensor:$" + input_ + ","
+                    else:
+                        ARGUMENTS = ARGUMENTS + " PD_Tensor_Array:$" + input_ + ","
+            # unsupported:   BLOCK = 8;  BLOCKS = 10;
+            attr_mlir_converter = {
+                0: 'SI32Attr',
+                1: 'F32Attr',
+                2: 'StrAttr',
+                3: 'I32ArrayAttr',
+                4: 'F32ArrayAttr',
+                5: 'StrArrayAttr',
+                6: 'BoolAttr',
+                7: 'BoolArrayAttr',
+                9: 'SI64Attr',
+                11: 'I64ArrayAttr'
+            }
+
+            # 2.3.2 attributes
+            for attr in op_proto[ATTRS]:
+                if (op_proto[ATTRS][attr][EXTRA] == True) or (
+                        attr in skipped_attr_list):
+                    continue
+                if op_proto[ATTRS][attr][DEFAULT_VALUE] != None:
+                    if op_proto[ATTRS][attr][TYPE] in attr_mlir_converter:
+                        default_value = str(op_proto[ATTRS][attr][
+                            DEFAULT_VALUE])
+                        if (attr_mlir_converter[op_proto[ATTRS][attr][TYPE]] in
+                            [
+                                'I32ArrayAttr', 'F32ArrayAttr', 'StrArrayAttr',
+                                'BoolArrayAttr', 'I64ArrayAttr'
+                            ]):
+                            default_value = default_value.replace(
+                                '[', '{').replace(']', '}')
+                        if (attr_mlir_converter[op_proto[ATTRS][attr][TYPE]] in
+                            ['BoolAttr', 'BoolArrayAttr']):
+                            default_value = default_value.lower()
+                        elif (attr_mlir_converter[op_proto[ATTRS][attr][TYPE]]
+                              in ['StrAttr', 'StrArrayAttr']):
+                            default_value = default_value.replace('\'', '\\\"')
+                            if attr_mlir_converter[op_proto[ATTRS][attr][
+                                    TYPE]] == "StrAttr":
+                                default_value = '\\\"' + default_value + '\\\"'
+                        attr_list = " DefaultValuedAttr<" + attr_mlir_converter[
+                            op_proto[ATTRS][attr]
+                            [TYPE]] + ", \"" + default_value + "\">:$" + attr + ","
+                        ARGUMENTS += attr_list
+                    else:
+                        print("Error:" + op_type + ":" + attr + ":" + str(
+                            op_proto[ATTRS][attr][TYPE]))
+                else:
+                    if op_proto[ATTRS][attr][TYPE] in attr_mlir_converter:
+                        attr_type_ = attr_mlir_converter[op_proto[ATTRS][attr][
+                            TYPE]]
+                        if (attr_type_ in [
+                                'I32ArrayAttr', 'F32ArrayAttr', 'StrArrayAttr',
+                                'BoolArrayAttr', 'I64ArrayAttr'
+                        ]):
+                            attr_list = attr_type_ + ":$" + attr + ","
+                            ARGUMENTS += attr_list
+                    else:
+                        print(" ouch Error:" + op_type + ":" + attr + ":" + str(
+                            op_proto[ATTRS][attr][TYPE]))
+            ARGUMENTS = ARGUMENTS[:-1] + ");\n"
+
+        # 2.4 results info
+        RESULTS = ""
+        if (len(op_proto[OUTPUTS]) > 0):
+            RESULTS = "\n  let results = (outs "
+            for output_ in op_proto[OUTPUTS]:
+                if op_proto[OUTPUTS][output_][EXTRA] != True and op_proto[
+                        OUTPUTS][output_][INTERMEDIATE] != True:
+                    if op_proto[OUTPUTS][output_][DUPLICABLE] != "true":
+                        RESULTS = RESULTS + "PD_Tensor:$" + output_ + ","
+                    else:
+                        RESULTS = RESULTS + "PD_Tensor_Array:$" + output_ + ","
+                        print(HEAD + " PD_Tensor_Array:$" + output_ + ",")
+
+            RESULTS = RESULTS[:-1] + ");\n"
+        with open(dst_dialect_file, 'a') as ops_mlir_file:
+            ops_mlir_file.write(HEAD)
+            ops_mlir_file.write(SUMMARY)
+            ops_mlir_file.write(DESCRIPTION)
+            ops_mlir_file.write(ARGUMENTS)
+            ops_mlir_file.write(RESULTS)
+            ops_mlir_file.write("}\n")
+
+    print("Skipped ops num: " + str(len(skipped_op_list)))
+    print("Automatically generated op dialects num: " + str(
+        len(automatically_generated_op_dialect)))
+
+    # 3. custom op dialect and end of file
+    with open(dst_dialect_file, 'a') as ops_mlir_file:
+        with open(custom_dialect_file, 'r') as custom_ops_file:
+            custom_ops = custom_ops_file.readlines()
+            ops_mlir_file.writelines(custom_ops)
+
+        end_ = "\n#endif  // PD_OPS"
+        ops_mlir_file.write(end_)
+
+
+if __name__ == "__main__":
+    all_op_protos_dict = get_all_ops_desc()
+    convert_op_proto_into_mlir(all_op_protos_dict)